2019/08/09 by Zhuojun Chen, Junhao Cheng, Chen, Zhuojun +9
Computer Science · Social Sciences · #Anomaly Detection Techniques and Applications #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Human Mobility and Location-Based Analysis #Video Surveillance and Tracking Methods
paper · pdf · doi:10.48550/arxiv.1908.03314
openalex publication_date 2019/08/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We seek to improve crowd counting as we perceive limits of currently prevalent density map estimation approach on both prediction accuracy and time efficiency. We leverage multilevel pixelation of density map as it helps improve SNR of training data and therefore, reduce prediction error. To achieve a better model, we introduce multilayer gradient fusion for training a density-aware global count regressor. More specifically, on training stage, a backbone network receives gradients from multiple branches to learn the density information, whereas those branches are to be detached to accelerate inference. By taking advantages of such method, our model improves benchmark results on public datasets and exhibits itself to be a new solution to crowd counting problems in practice.